In imaging science, image processing is any form of signal processing for which the input is an image, such as a photograph or a video frame; the output of image processing may be either an image or a set of characteristics or parameters related to the image. Most image-processing techniques involve treating the image as a two-dimensional (2D) signal and applying standard signal-processing techniques to it.
Image processing usually refers to digital image processing, but optical and analog image processing also are possible. The acquisition of images is referred to as imaging. Image processing refers to processing of a 2D picture by a computer. An image defined in the “real world” is considered to be a function of two real variables, for example, a(x,y) with a as the amplitude (e.g., brightness) of the image at the real coordinate position (x,y).
Modern digital technology has made it possible to manipulate multi-dimensional signals with systems that range from simple digital circuits to advanced parallel computers. The goal of this manipulation can be divided into three categories of Image Processing (image in ->image out), Image Analysis (image in ->measurements out), and Image Understanding (image in ->high-level description out).
An image may be considered to contain sub-images sometimes referred to as regions-of-interest, ROIs, or simply regions. This concept reflects the fact that images frequently contain collections of objects, each of which can be the basis for a region. In a sophisticated image processing system, it should be possible to apply specific image processing operations to selected regions. Thus, one part of an image (region) might be processed to suppress motion blur while another part might be processed to improve color rendition.
Most usually, image processing systems require that the images be available in digitized form, that is, arrays of finite length binary words. For digitization, the given Image is sampled on a discrete grid and each sample or pixel is quantized using a finite number of bits. The digitized image is processed by a computer. To display a digital image, it is first converted into analog signal, which is scanned onto a display.
Closely related to image processing are computer graphics and computer vision. In computer graphics, images are manually made from physical models of objects, environments, and lighting, instead of being acquired (via imaging devices such as cameras) from natural scenes, as in most animated movies. Computer vision, on the other hand, is often considered high-level image processing, out of which a machine/computer/software intends to decipher the physical contents of an image or a sequence of images (e.g., videos or three-dimensional (3D) full-body magnetic resonance scans).
In modern sciences and technologies, images also gain much broader scopes due to the ever-growing importance of scientific visualization (of often large-scale complex scientific/experimental data). Examples include microarray data in genetic research or real-time multi-asset portfolio trading in finance.
Thus, a need still remains for an image processing system to be developed. In view of the ever-increasing commercial competitive pressures, along with growing consumer expectations, it is critical that answers be found for these problems. Additionally, the need to reduce costs, improve efficiencies and performance, and meet competitive pressures adds an even greater urgency to the critical necessity for finding answers to these problems.
Solutions to these problems have been long sought but prior developments have not taught or suggested any solutions and, thus, solutions to these problems have long eluded those skilled in the art.